Paradigms

MI

Motor Imagery Paradigm.

class brainda.paradigms.imagery.MotorImagery(channels: Optional[List[str]] = None, events: Optional[List[str]] = None, intervals: Optional[List[Tuple[float, float]]] = None, srate: Optional[float] = None)

Bases: brainda.paradigms.base.BaseParadigm

Basic motor imagery paradigm.

is_valid(dataset)

Verify the dataset is compatible with the paradigm.

This method is called to verify dataset is compatible with the paradigm.

This method should raise an error if the dataset is not compatible with the paradigm. This is for example the case if the dataset is an ERP dataset for motor imagery paradigm, or if the dataset does not contain any of the required events.

Parameters

dataset (BaseDataset) – dataset

P300

P300 Paradigm.

class brainda.paradigms.p300.P300(channels: Optional[List[str]] = None, events: Optional[List[str]] = None, intervals: Optional[List[Tuple[float, float]]] = None, srate: Optional[float] = None)

Bases: brainda.paradigms.base.BaseParadigm

is_valid(dataset)

Verify the dataset is compatible with the paradigm.

This method is called to verify dataset is compatible with the paradigm.

This method should raise an error if the dataset is not compatible with the paradigm. This is for example the case if the dataset is an ERP dataset for motor imagery paradigm, or if the dataset does not contain any of the required events.

Parameters

dataset (BaseDataset) – dataset

SSVEP

SSVEP Paradigm.

class brainda.paradigms.ssvep.SSVEP(channels: Optional[List[str]] = None, events: Optional[List[str]] = None, intervals: Optional[List[Tuple[float, float]]] = None, srate: Optional[float] = None)

Bases: brainda.paradigms.base.BaseParadigm

is_valid(dataset)

Verify the dataset is compatible with the paradigm.

This method is called to verify dataset is compatible with the paradigm.

This method should raise an error if the dataset is not compatible with the paradigm. This is for example the case if the dataset is an ERP dataset for motor imagery paradigm, or if the dataset does not contain any of the required events.

Parameters

dataset (BaseDataset) – dataset

Base Paradigm

Base Paradigm Design.

class brainda.paradigms.base.BaseParadigm(channels: Optional[List[str]] = None, events: Optional[List[str]] = None, intervals: Optional[List[Tuple[float, float]]] = None, srate: Optional[float] = None)

Bases: object

Abstract Base Paradigm.

get_data(dataset: brainda.datasets.base.BaseDataset, subjects: Optional[List[Union[int, str]]] = None, label_encode: bool = True, return_concat: bool = False, n_jobs: int = - 1, verbose: Optional[bool] = None) → Tuple[Union[Dict[str, Union[numpy.ndarray, pandas.core.frame.DataFrame]], numpy.ndarray, pandas.core.frame.DataFrame], ...]

Get data from dataset with selected subjects.

Parameters

• dataset (BaseDataset) – dataset

• subjects (Optional[List[Union[int, str]]], optional) – selected subjects, by default None

• label_encode (bool, optional,) – if True, return y in label encode way

• return_concat (bool, optional) – if True, return concated ndarray object, otherwise return dict of events, by default False

• n_jobs (int, optional) – Parallel jobs, by default -1

• verbose (Optional[bool], optional) – verbose, by default None

Returns

Xs, ys, metas, corresponding to data, label and meta data

Return type

Tuple[Union[Dict[str, Union[np.ndarray, pd.DataFrame]], Union[np.ndarray, pd.DataFrame]], …]

Raises

TypeError – raise error if dataset is not avaliable for the paradigm

abstract is_valid(dataset: brainda.datasets.base.BaseDataset) → bool

Verify the dataset is compatible with the paradigm.

This method is called to verify dataset is compatible with the paradigm.

This method should raise an error if the dataset is not compatible with the paradigm. This is for example the case if the dataset is an ERP dataset for motor imagery paradigm, or if the dataset does not contain any of the required events.

Parameters

dataset (BaseDataset) – dataset

register_data_hook(hook)

Register data hook before return data.

Parameters

hook (callable object) –

Callable object to process ndarray data before return it. Its’ signature should look like:

hook(X, y, meta, caches) -> X, y, meta, caches

where caches is an dict storing information, X, y are ndarray object, meta is a pandas DataFrame instance.

register_epochs_hook(hook)

Register epochs hook after epoch operation.

Parameters

hook (callable object) –

Callable object to process Epochs object after epoch operation. Its’ signature should look like:

hook(epochs, caches) -> epochs, caches

where caches is an dict storing information, epochs is MNE Epochs instance.

register_raw_hook(hook)

Register raw hook before epoch operation.

Parameters

hook (callable object) –

Callable object to process Raw object before epoch operation. Its’ signature should look like:

hook(raw, caches) -> raw, caches

where caches is an dict stroing information, raw is MNE Raw instance.

unregister_data_hook()

Register data hook before return data.

unregister_epochs_hook()

Register epochs hook after epoch operation.

unregister_raw_hook()

Unregister raw hook before epoch operation.

brainda.paradigms.base.label_encoder(y, labels)